Improve outputs

app.py

@@ -103,23 +103,16 @@ rainier = InteractiveRainier()
 
 def predict(question, kg_model, qa_model, max_input_len, max_output_len, m, top_p):
     result = rainier.run(question, max_input_len, max_output_len, m, top_p)
-    output = ''
-    output += f'QA model answer without knowledge: {result["knowless_pred"]}\n'
-    output += f'QA model answer with knowledge: {result["knowful_pred"]}\n'
-    output += '\n'
-    output += f'All generated knowledges:\n'
-    for knowledge in result['knowledges']:
-        output += f'  {knowledge}\n'
-    output += '\n'
-    output += f'Knowledge selected to make the prediction: {result["selected_knowledge"]}\n'
-    return output
-
-description = '''This is a demo for the paper, <a href="https://arxiv.org/pdf/2210.03078.pdf" target="_blank">Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering</a>, presented in EMNLP 2022.
-[<a href="https://github.com/liujch1998/rainier" target="_blank">Code</a>] [<a href="https://huggingface.co/liujch1998/rainier-large" target="_blank">Model</a>]
-This demo is made & maintained by <a href="https://liujch1998.github.io/" target="_blank">Jiacheng (Gary) Liu</a>.
-
-Rainier is a knowledge-generating model that enhances the commonsense QA capability of a QA model.
-To try this model, select an example question, or write your own question in the suggested format.'''
+    # output = ''
+    # output += f'QA model answer without knowledge: {result["knowless_pred"]}\n'
+    # output += f'QA model answer with knowledge: {result["knowful_pred"]}\n'
+    # output += '\n'
+    # output += f'All generated knowledges:\n'
+    # for knowledge in result['knowledges']:
+    #     output += f'  {knowledge}\n'
+    # output += '\n'
+    # output += f'Knowledge selected to make the prediction: {result["selected_knowledge"]}\n'
+    return result['knowless_pred'], result['knowful_pred'], '\n'.join(result['knowledges']), result['selected_knowledge']
 
 examples = [
     'If the mass of an object gets bigger what will happen to the amount of matter contained within it? \\n (A) gets bigger (B) gets smaller',
@@ -141,12 +134,18 @@ input_m = gr.Slider(label='Number of generated knowledges:', value=10, mininum=1
     info='The actual number of generated knowledges may be less than this number due to possible duplicates.',
 )
 input_top_p = gr.Slider(label='top_p for knowledge generation:', value=0.5, mininum=0.0, maximum=1.0, step=0.05)
-output_text = gr.Textbox(label='Output', interactive=False)
+output_knowless_answer = gr.Textbox(label='QA model answer without knowledge:', interactive=False)
+output_knowful_answer = gr.Textbox(label='QA model answer with knowledge:', interactive=False)
+output_all_knowledges = gr.Textbox(label='All generated knowledges:', interactive=False)
+output_selected_knowledge = gr.Textbox(label='Knowledge selected to make the prediction:', interactive=False)
+
+description = '''This is a demo for the paper, [*Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering*](https://arxiv.org/pdf/2210.03078.pdf), presented at EMNLP 2022. [[Code](https://github.com/liujch1998/rainier)] [[Model](https://huggingface.co/liujch1998/rainier-large)] This demo is made & maintained by [Jiacheng (Gary) Liu](https://liujch1998.github.io).
+Rainier is a knowledge-generating model that enhances the commonsense QA capability of a QA model. To try this model, select an example question, or write your own commonsense question in the suggested format.'''
 
 gr.Interface(
     fn=predict,
     inputs=[input_question, input_kg_model, input_qa_model, input_max_input_len, input_max_output_len, input_m, input_top_p],
-    outputs=output_text,
+    outputs=[output_knowless_answer, output_knowful_answer, output_all_knowledges, output_selected_knowledge],
     title="Rainier Demo",
     description=description,
 ).launch()